Building Research Capacity and Organizational Empathy Among Students: Making Connections Beyond the Data

IntroductionThe leveraging of multi-sector, whole-population, linked administrative data is advantageous for conducting research on complex real-world problems. However, such large and complex data repositories can sometimes appear impersonal and overwhelming. Establishing organizational empathy (OE) in thecontext of a multi-sector partnership between academic, government and community representatives can help us understand the data better for social policy research. Evidence stemming from this research can then inform policy decisions, ultimately increasing the potency of linked data analysis and creating more meaningful student experiences. Our objective is to examine the role of OE in the student research experience. Objectives and ApproachSPECTRUM (Social Policy Evaluation Collaborative Team Research at Universities in Manitoba) is a multi-disciplinary partnership working to provide evidence-based solutions to ‘wicked’ social issues by using linked data from multiple sectors. SPECTRUM provides fellowships to students to become partners in the collaboration. Students have participated in quarterly workshops, building relationships with community leaders, government decision-makers and academic researchers. Students are from various faculties, bringing their unique frameworks and research interests to the collective. Through OE, students observeand participate in SPECTRUM, relating its goals and outcomes to society and their own research. ResultsStudent inclusion in SPECTRUM enhances the partnership by providing a greater range of perspectives and facilitates the development of OE among SPECTRUM members. Students are using linked administrative data, while actively engaging in dialogue with stakeholders, thereby enriching their knowledge and understanding of research. Conclusion / ImplicationsData linkage involves more than just use of the repository; it requires establishing common ground since the data have different meaning to each partner. OE developed through SPECTRUM provides invaluable insight into and context for the data. Knowledge transfer among members of the partnership will enrich SPECTRUM’s research outcomes while building capacity among Students.

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P4343Reconciling acute coronary syndrome diagnoses between linked administrative data and hospital medical records in medical research

European Heart Journal ◽

10.1093/eurheartj/ehz745.0751 ◽

2019 ◽

Vol 40 (Supplement_1) ◽

Author(s):

K Hyun ◽

J Redfern ◽

T Briffa ◽

D Chew ◽

J French ◽

...

Keyword(s):

Acute Coronary Syndrome ◽

Administrative Data ◽

Medical Records ◽

Linked Data ◽

Factors Associated ◽

Coronary Syndrome ◽

Hospital Medical ◽

Icd 10 ◽

Linked Administrative Data ◽

Heart Foundation

Abstract Background/Introduction Administrative data incorporating the International Classification of Diseases 10th Revision (ICD-10) is commonly used in cardiac research. Using patient records, diagnoses are systematically coded by trained coders who have limited/no clinical experience. Therefore, it is important to understand how systematically coded cardiac diagnoses compare with clinically assessed diagnoses to better analyse and interpret studies that have used linked administrative data to adjudicate patient's diagnosis. Purpose To assess the agreement between the acute coronary syndrome (ACS) diagnoses according to linked data compared to those extracted from hospital medical records by clinicians participating in a national registry and determine the factors associated with diagnoses disagreement. Methods The rate of ST-elevation myocardial infarction (STEMI), non-STEMI (NSTEMI) and unstable angina (UA) obtained from the medical records, from admission to discharge, for the nationwide SNAPSHOT ACS audit in 2012 were compared to the corresponding ICD-10 Australian Modification (ICD-10-AM) codes using linked data from 6 jurisdictions covering all Australian states (6) and territories (2). The proportions of the overall agreement (OA), the positive agreement (PA) and the Cohen's weighted kappa and the 95% confidence interval (CI) were derived using both data sources for STEMI, NSTEMI and UA individually, where kappa≥0.8 confers strong agreement and 0.6≤kappa<0.8 moderate agreement. The factors associated with the diagnostic disagreement were explored using multilevel multivariable logistic regression model (backward selection method), accounting for the hospital clustering effect. Results Overall, 3130 patients had both medical records and linked data available for comparison. The degree of agreement was greatest for STEMI and lowest for UA (STEMI: OA=97%, PA=85%, kappa (95% CI)=0.84 (0.81, 0.87); NSTEMI: OA=91%, PA=81%, kappa (95% CI)=0.76 (0.73,0.79); UA: OA = 81%, PA=53%, kappa (95% CI)=0.41 (0.38, 0.45)). Further, the independent factors associated with the disagreement between the medical records and the linked data were the diagnosis of UA (UA vs. STEMI (odds ratio (95% CI)): 6.85 (4.12, 11.40)), not receiving revascularisation (2.27 (1.69, 3.03)), and the state where the ICD-10-AM was coded (p=0.007) (see Figure). Figure 1 Conclusion This study suggests that the agreement between the systematically coded diagnoses from linked administrative data and the diagnosis from the clinical assessment is greater in patients who received revascularisation and worse in those with UA. Also, the degree of agreement varies between states. As the linked data and the ICD codes are being used more often in research to support the evidence-based policies and practice, more attention is needed in testing and improving the accuracy of the ICD-10 codes as well as the ICD-11 codes that are soon to be introduced. Acknowledgement/Funding KH is funded by Heart Foundation Postdoctoral Fellowship. SNAPSHOT data linkage project was funded by the NSW Heart Foundation CVRN Project Grant

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Symposium on Using Administrative Data for Social Policy Research

Public Administration Review ◽

10.1111/puar.12538 ◽

2016 ◽

Vol 76 (2) ◽

pp. 368-368

Keyword(s):

Social Policy ◽

Administrative Data ◽

Policy Research

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Integrated Administrative Data for Early Childhood Iowa: A Governance Model to inform Policy and Program Collaboration

International Journal for Population Data Science ◽

10.23889/ijpds.v3i5.1067 ◽

2018 ◽

Vol 3 (5) ◽

Author(s):

Heather L Rouse ◽

Cassandra J Dorius ◽

Jeffery Anderson ◽

Elizabeth JD Richey

Keyword(s):

Early Childhood ◽

Social Policy ◽

Administrative Data ◽

Ad Hoc ◽

Policy Research ◽

Policy Network ◽

Data Systems ◽

Executive Leaders ◽

Governance Model ◽

Critical Tension

In response to demands on public systems to do more, do better, and cost less, the value of integrated administrative data systems (IDS) for social policy is increasing (Fantuzzo & Culhane, 2016). This is particularly relevant in programming for young children where services are historically fragmented, disconnected from systems serving school-aged children, and siloed among health, human services, and education agencies. Guided by the vision that Iowa’s early childhood system will be effectively and efficiently coordinated to support healthy families, we are developing an early childhood IDS to address this disconnection and facilitate relevant and actionable social policy research. Iowa’s IDS is a state-university partnership that acknowledges the need for agencies to retain control of their data while enabling it to be integrated across systems for social policy research. The innovative governance model deliberately incorporates procedures for stakeholder engagement at critical tension points between executive leaders, program managers, researchers, and practitioners. Standing committees (Governance Board, Data Stewardship, and Core team) authorize and implement the work of the IDS, while ad-hoc committees are solicited for specific projects to advise and translate research into practice. This paper will articulate the Iowa IDS governance model that was informed by means tested principles articulated by the Actionable Intelligence for Social Policy Network. It will include our collaborative development process; articulated mission and principles that guided discussions about legal authorization, governance, and use cases; and the establishment of governance committees to implement our vision for ethical and efficient use of administrative data for social policy.

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Symposium on Using Administrative Data for Social Policy Research

Public Administration Review ◽

10.1111/puar.12575 ◽

2016 ◽

Vol 76 (3) ◽

pp. 525-525

Keyword(s):

Social Policy ◽

Administrative Data ◽

Policy Research

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Non-ordained

Fieldwork in Religion ◽

10.1558/firn.32964 ◽

2017 ◽

Vol 11 (2) ◽

pp. 137-156 ◽

Cited By ~ 1

Author(s):

Jeanine Kraybill

Keyword(s):

Social Policy ◽

Women Religious ◽

Catholic Church ◽

Social Issues ◽

Female Leadership ◽

Religious Leaders ◽

Religious Congregations ◽

Effect Change ◽

Policy Goals ◽

Institutional Church

The Catholic Church, constructed on an all-male clerical model, is a hierarchical and gendered institution, creating barriers to female leadership. In interviewing members of the clergy and women religious of the faith, this article examines how female non-ordained and male clerical religious leaders engage and influence social policy. It specifically addresses how women religious maneuver around the institutional constraints of the Church, in order to take action on social issues and effect change. In adding to the scholarship on this topic, I argue that part of the strategy of women religious in navigating barriers of the institutional Church is not only knowing when to act outside of the formal hierarchy, but realizing when it is in the benefit of their social policy objectives to collaborate with it. This maneuvering may not always safeguard women religious from institutional scrutiny, as seen by the 2012 Doctrinal Assessment of the Leadership Conference of Women Religious, but instead captures the tension between female religious and the clergy. It also highlights how situations of institutional scrutiny can have positive implications for female religious leaders, their policy goals and congregations. Finally, this examination shows how even when women are appointed to leadership posts within the institutional Church, they can face limitations of acceptance and other constraints that are different from their female religious counterparts working within their own respective religious congregations or outside organizations.

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Big Data to Knowledge: Application of Machine Learning to Predictive Modeling of Therapeutic Response in Cancer.

Current Genomics ◽

10.2174/1389202921999201224110101 ◽

2020 ◽

Vol 21 ◽

Author(s):

Sukanya Panja ◽

Sarra Rahem ◽

Cassandra J. Chu ◽

Antonina Mitrofanova

Keyword(s):

Machine Learning ◽

Missing Values ◽

Therapeutic Response ◽

Patient Data ◽

Machine Learning Techniques ◽

Support Vector ◽

Complex Data ◽

Human Machine Interaction ◽

Data Repositories ◽

Response Modeling

Background: In recent years, the availability of high throughput technologies, establishment of large molecular patient data repositories, and advancement in computing power and storage have allowed elucidation of complex mechanisms implicated in therapeutic response in cancer patients. The breadth and depth of such data, alongside experimental noise and missing values, requires a sophisticated human-machine interaction that would allow effective learning from complex data and accurate forecasting of future outcomes, ideally embedded in the core of machine learning design. Objective: In this review, we will discuss machine learning techniques utilized for modeling of treatment response in cancer, including Random Forests, support vector machines, neural networks, and linear and logistic regression. We will overview their mathematical foundations and discuss their limitations and alternative approaches all in light of their application to therapeutic response modeling in cancer. Conclusion: We hypothesize that the increase in the number of patient profiles and potential temporal monitoring of patient data will define even more complex techniques, such as deep learning and causal analysis, as central players in therapeutic response modeling.

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